BJJ Datasets
Comprehensive datasets for BJJ research, analytics, and data science projects
Access curated datasets containing tournament results, fighter statistics, technique analysis, and motion capture data. Perfect for researchers, analysts, and developers building BJJ applications.
What are Different Types of BJJ Datasets?
BJJ datasets come in various formats and serve different analytical needs. From comprehensive tournament data and academic motion capture studies to computer vision training sets and dynamic ranking systems, each type offers unique insights into the world of Brazilian Jiu-Jitsu. Understanding these different categories helps researchers and practitioners choose the right data for their specific goals.
BJJ Dataset Types
Tournament & Competition Datasets
Match results, fighter statistics, and tournament outcomes
Tournament datasets capture the competitive landscape of Brazilian Jiu-Jitsu, providing detailed information about match outcomes, fighter performance, and competition results. These datasets are essential for understanding competitive patterns, ranking systems, and strategic analysis. Competition data includes match-by-match breakdowns, submission types, time to finish, and performance metrics across different weight classes and belt levels. Researchers and analysts use this data to study fighting styles, identify successful strategies, and develop predictive models for tournament outcomes.
Match-by-match tournament results and statistics
Fighter performance metrics by weight class and belt level
Submission type analysis and time-to-finish data
Tournament outcome predictions and strategic insights
Technique & Movement Datasets
Movement patterns, technique classifications, and biomechanical data
Technique datasets focus on the physical movements, patterns, and biomechanical aspects of BJJ. These collections include video annotations, motion capture data, and detailed classifications of techniques across different positions and transitions. From academic motion capture studies to practitioner-curated technique libraries, these datasets help researchers understand how techniques are performed, their effectiveness rates, and the biomechanical principles underlying successful BJJ movements. They serve as training resources for practitioners and research materials for sports scientists.
Video annotations and motion capture data
Technique classification by position and transition
Biomechanical analysis of movement patterns
Effectiveness rates and performance metrics
Computer Vision & Machine Learning Datasets
Labeled images and videos for AI training and automated analysis
Computer vision datasets contain labeled images and videos specifically designed for training machine learning models to recognize and analyze BJJ techniques. These datasets are crucial for developing automated technique recognition, pose estimation, and performance analysis tools. From basic technique classification to advanced pose detection, these datasets enable the development of AI-powered training assistants, automated judging systems, and sophisticated analytics platforms that can process BJJ footage at scale.
Labeled images and videos for AI training
Automated technique recognition systems
Pose estimation and movement analysis
Real-time performance feedback tools
Ranking & Performance Tracking Datasets
Dynamic ranking systems and performance metrics
Ranking datasets provide dynamic, real-time tracking of BJJ competitor performance through sophisticated rating systems like ELO. These datasets continuously update as new matches are recorded, offering a living snapshot of the competitive landscape. Beyond simple win/loss records, these systems incorporate match quality, opponent strength, and competition level to provide nuanced performance metrics. They help identify rising competitors, track career trajectories, and inform matchmaking decisions in tournaments.
Dynamic ELO-based ranking systems
Real-time performance tracking and updates
Match quality and opponent strength analysis
Career trajectory and competitor development insights
Academic & Research Datasets
University-led studies and scholarly research data
Academic datasets represent rigorous, peer-reviewed research into BJJ from universities and research institutions worldwide. These collections often combine multiple data types - from motion capture to statistical analysis - to advance our understanding of the sport. University researchers contribute unique perspectives, combining sports science expertise with advanced analytical techniques. Their work often explores areas like injury prevention, performance optimization, and the broader implications of martial arts training on human physiology and psychology.
Peer-reviewed university research studies
Multi-disciplinary data combining motion capture and statistics
Injury prevention and performance optimization research
Sports science analysis of martial arts physiology
BJJ Datasets for Research & Analysis
As the BJJ data science community grows, so does the availability of high-quality datasets for research and analysis. Our curated collection features comprehensive tournament data from major events like ADCC, academic research from universities like Ljubljana, and specialized datasets from institutions like Carnegie Mellon. Particularly noteworthy are the ADCC Matches and Fighter Stats datasets, which provide detailed competition data perfect for tournament analysis and predictive modeling.
BJJ Datasets
Grappling Techniques Dataset
Comprehensive technique classification and analysis
This extensive dataset provides a detailed classification of grappling techniques across multiple martial arts disciplines, with particular depth in Brazilian Jiu-Jitsu. Created by researchers, it offers systematic categorization of techniques by position, type, and effectiveness. The dataset has been instrumental in developing automated technique recognition systems and has enabled researchers to study technique frequency patterns across different grappling styles. Its comprehensive nature makes it valuable for both academic research and practical applications in technique analysis.
Systematic technique categorization by position and type
Multi-disciplinary grappling technique analysis
Automated technique recognition development
Technique frequency pattern research
ADCC Matches Dataset
Complete historical tournament data from ADCC competitions
The ADCC (Abu Dhabi Combat Club) Matches dataset represents one of the most comprehensive collections of high-level BJJ competition data available. It contains detailed match results from ADCC tournaments, widely regarded as the pinnacle of submission grappling competitions. This dataset has powered numerous research projects studying competitive BJJ strategies, with notable findings including the dominance of guard play at elite levels and the statistical correlation between submission variety and competitive success. It's frequently used for tournament analysis and has influenced coaching methodologies worldwide.
Complete ADCC tournament match results
Elite-level submission grappling competition data
Competitive strategy and guard play analysis
Statistical correlation studies for success factors
ADCC Fighter Statistics
Comprehensive fighter performance data from ADCC competitions
Building on the ADCC matches data, this dataset provides detailed statistical profiles of individual competitors across ADCC tournaments. It includes win/loss records, submission preferences, performance by weight class, and career trajectories of elite grapplers. The dataset has revealed fascinating insights about fighter development, including the correlation between submission diversity and long-term success, and the impact of weight class transitions on competitive performance. It's been used to develop predictive models for match outcomes and has informed training methodologies for aspiring competitors.
Individual competitor statistical profiles
Win/loss records and submission preferences
Weight class performance analysis
Career trajectory and fighter development insights
University of Ljubljana BJJ Dataset
Academic research on BJJ movement patterns and biomechanics
Created by researchers at the University of Ljubljana, this academic dataset represents pioneering work in applying computer vision and biomechanical analysis to Brazilian Jiu-Jitsu. It contains detailed movement pattern data captured through advanced motion tracking technology. The research has produced groundbreaking insights into the biomechanics of BJJ techniques, including optimal movement patterns for efficiency and injury prevention. The dataset has been cited in numerous academic papers and has influenced both training methodologies and injury prevention strategies in combat sports.
Advanced motion tracking technology data
Biomechanical analysis of BJJ techniques
Optimal movement patterns for efficiency
Injury prevention research insights
BJJ Disorientation Research
Motion capture data focusing on spatial disorientation in BJJ
This innovative dataset from Carnegie Mellon's Studio for Creative Inquiry explores the concept of spatial disorientation in BJJ through advanced motion capture technology. It examines how grapplers manipulate spatial awareness during transitions and submissions. The research has led to novel insights about cognitive aspects of BJJ, including how masters create and exploit moments of disorientation. This work has influenced not just BJJ training but also broader research into spatial cognition and motor learning in complex physical activities.
Spatial disorientation motion capture data
Cognitive aspects of BJJ transitions
Master-level disorientation techniques
Spatial cognition research applications
BJJ Technique Detection Dataset
Computer vision training data for automated technique recognition
This dataset provides labeled images and video frames specifically designed for training machine learning models to automatically detect and classify BJJ techniques. It serves as a foundation for developing AI-powered training assistants and automated analysis tools. The dataset has enabled the development of real-time technique recognition systems that can analyze live BJJ matches, providing instant feedback to practitioners and coaches. This technology has the potential to revolutionize BJJ training by making expert analysis more accessible and scalable.
Labeled images for machine learning training
Automated BJJ technique recognition
Real-time match analysis capabilities
AI-powered training assistant foundation
Extended BJJ Computer Vision Dataset
Expanded collection for advanced machine learning applications
An extended version of the BJJ computer vision dataset, this collection provides more comprehensive training data for developing sophisticated AI systems capable of advanced BJJ analysis. It includes varied angles, lighting conditions, and technique variations. The dataset has enabled the creation of advanced computer vision systems that can track technique execution quality, identify common errors, and provide personalized training recommendations. This technology represents the cutting edge of sports analytics in martial arts.
Comprehensive training data with varied conditions
Advanced AI system development
Technique execution quality tracking
Personalized training recommendation systems
Dynamic BJJ Rankings Platform
Real-time ELO-based ranking system for BJJ competitors
This dynamic platform provides real-time ELO rankings for BJJ competitors based on tournament results and match outcomes. Unlike static ranking systems, it continuously updates as new competitions are recorded, offering a living snapshot of the competitive BJJ landscape. The system has become an essential tool for competitors, coaches, and tournament organizers, providing objective performance metrics that account for match quality and opponent strength. Its transparent methodology has helped standardize ranking discussions in the BJJ community.
Real-time ELO-based ranking updates
Dynamic competitor performance tracking
Objective metrics accounting for match quality
Transparent ranking methodology standardization
BJJ Datasets FAQ
Everything you need to know about BJJ datasets, research applications, and data analysis